Cognitive Inference and Signal Processing in Adversarial Uncertain Environments

为了收集数据，处理并使其可用于广泛的潜在应用并易于访问，各种网络技术正在高度分布式架构中出现，用于增加异构传感器的数量和类型。建议的研究计划（RP）的目标是发明和分析认知分布式算法处理数据。传统的方法在实践中往往失败，因为它们的设计没有考虑到不确定和对抗性的环境（通信，认知被动雷达，压缩感知和稀疏信号处理，心房颤动映射，机器学习，高光谱成像和递归贝叶斯估计）。这项研究的成果是以增强环境传感和监测的新方法的形式，例如使用多种类型和数量的分布式传感器（如雷达，摄像机，电极）定位和检测源和物体。该RP将以先进和低成本的认知信号处理算法的形式产生解决方案，并发明新方法，使关键系统能够在U& AE中学习和认知操作。这些解决方案将应用于各个行业，例如：能源，通信，汽车，雷达，安全，生物医学，金融和大数据。这些部门的进步将对加拿大经济和就业产生重大影响，因为相关部门每年产生大量收入。加拿大的工业也将从这一RP的成果中受益，因为认知范式促进了更有效的资源利用，它们在提高效率方面的成功将使它们更具竞争力。此外，六名博士，四名硕士和五名本科生将接受前沿技术培训，以帮助满足该领域日益增长的专业知识需求。** 这项研究的及时性得到了最近多个行业在智能，机器学习和信号处理领域创造的大量就业机会的证明。经过培训的HQP能够胜任这些必要的角色，这对于支持加拿大企业的盈利性投资至关重要。

To collect data, process and make it available and readily accessible for a wide range of potential applications, various cyber technologies are emerging in a highly distributed architecture for increasing numbers and types of heterogeneous sensors. The goal of the proposed research program (RP) is to invent and analyze cognitive distributed algorithms for processing data. Traditional methods often fail in practice because they are not designed to take into account uncertain and adversarial environments (Umunication, cognitive passive radars, compressed sensing and sparse signal processing, atrial fibrillation mapping, machine learning, hyper-spectral imaging and recursive Bayesian estimation. The outcomes of this research are in the form of novel methods for enhanced sensing and monitoring of the environment such as localization and detection of sources and objects using multiple types and number of distributed sensors such as radars, cameras, electrodes. ******This RP will yield solutions in the form of advanced and low-cost cognitive signal processing algorithms and invent new methods to enable critical systems to learn and cognitively operate in U&AEs. These solutions will have applications in various industries such as: energy, communication, automotive, radar, security, biomedical, financial and big data. Advances in these sectors will have a significant impact upon the Canadian economy and employment, as relevant sectors generate significant annual revenue. Canada's industries will also stand to benefit from the outcomes of this RP because the cognitive paradigm promotes more efficient resource use, and their success in increasing efficiency will make them more competitive. In addition, six PhD, four MSc and five undergraduate students will be trained in leading-edge technology to help meet the growing demand for expertise in this area. ******The timeliness of this research is evidenced by recent massive job creation by multiple industries in the areas of intelligence, machine learning, and signal processing. HQP trained to fill these necessary roles are critical to supporting profitable investment in Canadian business.